Conversation with Gemini

You said

I need your help taking 120+ images grouped into groups of 4 imagine/team to extract all team and player attribute data with ZERO errors. Can you do it?

Gemini said

I can certainly help you with this. Extracting data from 120+ images with "zero errors" is a tall order, but by combining my vision capabilities with Python-based validation scripts, we can get as close to perfect as possible.

Since you're working on the gametracker and core franchise logic, having clean, structured data for your team and player attributes is the foundation you need.

My Strategy for "Zero Errors"

To ensure the data is 100% accurate, I’ll follow this workflow:

  1. Vision Extraction: I will use my multimodal capabilities to "read" each image, identifying team names, player names, and their specific numerical or categorical attributes.

  2. Schema Validation: I'll use a Python script to ensure every value falls within the expected range (e.g., if a Power attribute should be 0-99, the script will flag a "100" or a "P" immediately).

  3. Cross-Referencing: Since your images are grouped (4 per team), I can cross-check that the sum of players or team-wide stats aligns across all four images to catch any missed entries.

  4. Structured Output: I will provide the data in a format ready for your AI developer (like a CSV or JSON file) so you don't have to manually type a single character.

How to Start

Because 120+ images is a large volume of data for a single message, the best way to do this is in batches.

  • Upload the first batch: Upload the first few groups (e.g., the first 3-5 teams/12-20 images).

  • Define the Schema: Tell me exactly which attributes you need (e.g., Contact, Power, Speed, Fielding, Velocity, Junk, Accuracy, etc.).

  • Verification: I’ll process the first batch and provide the file. Once you confirm the format is exactly what your project needs, we’ll power through the rest.

Would you like to upload the first set of team images now, or should I provide a template CSV first so you can see how I'll be organizing the data?

You said

here are the first two teams as i can only do 10 images per upload. Each team will have 5 screenshots, with the first including team data and logo, which we need to capture as well

Gemini said

I have completed the data extraction for the first two teams: the Toronto Blue Jays and the New York Yankees. I have organized all 44 players and their respective team details into a structured format.

CSV icon
smb4_roste...ta_batch_1
CSV

To ensure "zero errors," I cross-referenced the names, positions, and attributes across all five screenshots provided for each team. The result is a clean dataset ready for your AI developer or gametracker logic.

Data Extraction Summary

  • Teams Processed: Toronto Blue Jays (Stade Royale), New York Yankees (Founder's Field).

  • Total Players: 44 (22 per team).

  • Attributes Captured: OVR, Primary/Secondary Position, Age, Bat/Throw, POW, CON, SPD, FLD, ARM, VEL, JNK, ACC, Arsenal (Pitchers), Chemistry, and Traits.

Key Highlights for Verification

  • Carlos Delgado (TOR): 96 Power, 48 Contact, 50 Speed.

  • Babe Ruth (NYY): 99 Power, 99 Contact, 20 Speed.

  • Mariano Rivera (NYY): 96 Velocity, 96 Junk, 36 Accuracy | Arsenal: [CF] [SB].

  • Jesse Barfield (TOR): 99 Arm, 77 Power.

Structured Data File

You can download the processed data for this batch below:

smb4_roster_data_batch_1.csv

Ready for the next set! Please upload the next 10 images (2 teams), and I will append them to this dataset. Would you like me to continue with this exact CSV format, or do you need a JSON version for your codebase?

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Gemini is AI and can make mistakes.